A spatio-temporal deep learning model for short-term bike-sharing demand prediction
Journal article, 2023

Bike-sharing systems are widely operated in many cities as green transportation means to solve the last mile problem and reduce traffic congestion. One of the critical challenges in operating high-quality bike-sharing systems is rebalancing bike stations from being full or empty. However, the complex characteristics of spatiotemporal dependency on usage demand may lead to difficulties for traditional statistical models in dealing with this complex relationship. To address this issue, we propose a graph-based neural network model to learn the representation of bike-sharing demand spatial-temporal graph. The model has the ability to use graph-structured data and takes both spatial -and temporal aspects into consideration. A case study about bike-sharing systems in Nanjing, a large city in China, is conducted based on the proposed method. The results show that the algorithm can predict short-term bike demand with relatively high accuracy and low computing time. The predicted errors for the hourly station level usage demand prediction are often within 20 bikes. The results provide helpful tools for short-term usage demand prediction of bike-sharing systems and other similar shared mobility systems.

demand forecast

graph neural network

deep learning

artificial intelligence

Author

Ruo Jia

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Richard Chamoun

Student at Chalmers

Alexander Wallenbring

Student at Chalmers

Masoomeh Advand

Islamic Azad University

Shanchuan Yu

China Merchants Chongqing Communications Research & Design Institute

Yang Liu

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Kun Gao

Chalmers, Architecture and Civil Engineering, Geology and Geotechnics

Electronic Research Archive

26881594 (eISSN)

Vol. 31 2 1031-1047

Subject Categories

Computer Engineering

Computer Science

Computer Systems

Driving Forces

Sustainable development

Areas of Advance

Transport

DOI

10.3934/era.2023051

More information

Latest update

5/23/2024